Extra Data

How many flies I counted, and when

Experiments with fruit flies can involve counting large numbers, particularly when flies are scored as part of a fitness assay. I was curious how many flies I scored in various projects, so I dug back through my files.

This plot shows the number of flies scored and the approximate date the scoring was completed (scoring for a large assay can take days or weeks, or even months if you freeze them to score later). Connected points show the cumulative total. I considered only projects where I was lead author, because those are the cases where (a) I have the best access to the raw data, and (b) I did most or all of the scoring myself (I was involved in four other fly projects during this time period). One project involved three major assays, which are shown separately.

2.7 million seems like it might be a lot. The effective population size of African D. melanogaster is thought to be about 1.15 million*. But there are probably more flies hanging around the dump. The nearly-linear increase in the total over time suggests longer prep time for larger assays, which makes sense. Half of the total comes from just one project, although the work was spread out.

*(Shapiro et al., 2007, PNAS)


Sizing up yeast cells

For various reasons I wanted to know how big some of my yeast strains were, in terms of cell size. So we borrowed a device called a FlowCam, which automatically takes pictures of many thousands of cells as they go whizzing past a microscope lens. Here’s what the images typically look like.

Yeast love to divide, and so although many of the images are of single cells, there are lots of doublets and some larger clumps. The FlowCam software automatically estimates the dimensions of each particle. Here I’ve plotted the data from one sample, applying kernel-density estimation to a grid of aspect ratio vs. volume.

The hot spot in the upper left represents single cells, which have a relatively “round” aspect ratio (closer to 1). There’s also a hot spot of larger, more elongated particles, which are the doubled cells.


Applications I received for a WorkLearn position

It’s great to have undergrads helping out with research in the lab, and one way that happens at UBC is through the WorkLearn program. I sensed some trends in when people submitted their applications. Here are the data on total applications over time, with the inset showing the time of day applications were submitted.

Applications came in steadily through the first week up until Friday, which happened to be St. Patrick’s Day, and then there was a notable drop-off over the weekend. The rate accelerated over the second week, right up to the final minutes before the deadline. Most applications were submitted in the evening, driven partly – but not entirely – by the rush at the end. I was relieved to see that no one submitted an application between 4:00 AM and 6:00 AM.


Lab work and lots of it

During my PhD I used Excel spreadsheets to keep track of most of my lab work. This approach was useful for general organization, and was critical for arranging the schedule to (mostly) avoid major holidays and conferences. Now I can look back and see which days were occupied. I calculated the percent of days with scheduled lab work within a 28-day sliding window, using 7-day increments over a period of ~1150 days.

It seems I managed to avoid doing lab work on the weekends except when I didn’t. Occupied days stayed at or above 5/7 for a couple of years. There’s some interesting periodicity, which may be due in part to the 2-week maintenance cycle often used for fly stocks.